Detect highway lane lines from a video stream. Use OpenCV image analysis techniques to identify lines, including Hough transforms and Canny edge detection.

• Machine Learning: Review fundamentals of machine learning, including regression and classification.

• Neural Networks: Learn about perceptrons, activation functions, and basic neural networks. Implement your own neural network in Python.

• Logistic Classifier: Study how to train a logistic classifier, using machine learning. Implement a logistic classifier in TensorFlow.

• Optimization: Investigate techniques for optimizing classifier performance, including validation and test sets, gradient descent, momentum, and learning rates.

• Rectified Linear Units: Evaluate activation functions and how they affect performance.

• Regularization: Learn techniques, including dropout, to avoid overfitting a network to the training data.

• Convolutional Neural Networks: Study the building blocks of convolutional neural networks, including filters, stride, and pooling.

Implement and train a convolutional neural network to classify traffic signs. Use validation sets, pooling, and dropout to choose a network architecture and improve performance.

• Keras: Build a multi-layer convolutional network in Keras. Compare the simplicity of Keras to the flexibility of TensorFlow.

• Transfer Learning: Finetune pre-trained networks to solve your own problems. Study cannonical networks such as AlexNet, VGG, GoogLeNet, and ResNet.

Architect and train a deep neural network to drive a car in a simulator. Collect your own training data and use it to clone your own driving behavior on a test track.

• Cameras: Learn the physics of cameras, and how to calibrate, undistort, and transform image perspectives.

• Lane Finding: Study advanced techniques for lane detection with curved roads, adverse weather, and varied lighting.

Detect lane lines in a variety of conditions, including changing road surfaces, curved roads, and variable lighting. Use OpenCV to implement camera calibration and transforms, as well as filters, polynomial fits, and splines.

• Support Vector Machines: Implement support vector machines and apply them to image classification.

• Decision Trees: Implement decision trees and apply them to image classification.

• Histogram of Oriented Gradients: Implement histogram of oriented gradients and apply it to image classification.

• Deep Neural Networks: Compare the classification performance of support vector machines, decision trees, histogram of oriented gradients, and deep neural networks.

• Vehicle Tracking: Review how to apply image classification techniques to vehicle tracking, along with basic filters to integrate vehicle position over time.

• Track vehicles in camera images using image classifiers such as SVMs, decision trees, HOG, and DNNs. Apply filters to fuse position data.